Synovial sarcoma (SS) is an aggressive cancer occurring mainly in young adults that contains in >95% of cases a t(X;18) fusing SYT to either SSX1 or SSX2. The chimeric SYT-SSX product appears to function as an aberrant transcriptional protein to deregulate gene expression but its critical target genes remain unknown. In Project 4, we plan to integrate our existing Affymetrix expression profiling data with RNA interference (RNAi), chromatin immunoprecipitation (ChrIP), bioinformatics, and re-sequencing to gain insights into SS pathogenesis and to identify new methods for prognostication and targeted therapy. Aim 1. Analysis of signaling pathways in SS. Leads provided by our expression profiling data into signaling pathways involved in the biology of SS will be pursued, including the SHH/GLI, Wnt/beta-catenin, and selected kinase pathways. Functional and mutational studies of these pathways will be performed and the expression profiles associated with their activation will be determined. Aim 2. Analysis of SYT-SSX-regulated gene expression. To define the targets of SYT-SSX in SS, we will perform expression profiling of SYT-SSX knockdown in SS cell lines and ChrIP analysis of candidate SYT-SSX targets. We will also use bioinformatic approaches to identify recurrent motifs in the promoters of candidate SYT-SSX target genes drawn from the same two sources. Such motifs might provide leads in identifying transcription factor families that interact with SYT-SSX and mediate its effects. SYT-SSX target genes will then be validated as transcriptional targets. Finally, selected SYT-SSX target genes will be evaluated as potential therapeutic targets by assessing their role in SS growth using RNAi knockdown. Aim 3. Development of expression profiling-based clinical predictors in SS. Two approaches will be used to identify prognostically significant genes within the expression profile of SS, using existing and prospective microarray data. First, a predictor of distant recurrence-free survival will be developed and then combined with a new nomogram predictor based on clinical variables. Secondly, the expression signature of metastatic potential will be defined based on metastatic and non-metastatic SS samples and used to develop a prediction model for metastatic potential. The genes composing these predictors may provide insights into the determinants of clinical behavior in SS and opportunities for improved assignment of SS patients into high risk and low risk subgroups.